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Cited by 304AI and HR TechnologiesEmployer Branding and e-HRMOnline Learning and Analytics

The DOI matches its Crossref record; 61 of the 61 references it cites were found in a registry or an index.

Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming

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Pessach, Dana, Singer, Gonen, Avrahami, Dan, Chalutz Ben-Gal, Hila, Shmueli, Erez, Ben-Gal, Irad·2020·Decision Support Systems
DOI 10.1016/j.dss.2020.113290PMC PMC7252110
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Abstract

In this paper, we propose a comprehensive analytics framework that can serve as a decision support tool for HR recruiters in real-world settings in order to improve hiring and placement decisions. The proposed framework follows two main phases: a local prediction scheme for recruitments' success at the level of a single job placement, and a mathematical model that provides a global recruitment optimization scheme for the organization, taking into account multilevel considerations. In the first phase, a key property of the proposed prediction approach is the interpretability of the machine learning (ML) model, which in this case is obtained by applying the Variable-Order Bayesian Network (VOBN) model to the recruitment data. Specifically, we used a uniquely large dataset that contains recruitment records of hundreds of thousands of employees over a decade and represents a wide range of heterogeneous populations. Our analysis shows that the VOBN model can provide both high accuracy and interpretability insights to HR professionals. Moreover, we show that using the interpretable VOBN can lead to unexpected and sometimes counter-intuitive insights that might otherwise be overlooked by recruiters who rely on conventional methods. We demonstrate that it is feasible to predict the successful placement of a candidate in a specific position at a pre-hire stage and utilize predictions to devise a global optimization model. Our results show that in comparison to actual recruitment decisions, the devised framework is capable of providing a balanced recruitment plan while improving both diversity and recruitment success rates, despite the inherent trade-off between the two.

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Research works printed but not linked: 4

  1. Bernthal, 2001, Retaining talent: a benchmarking study, HR Benchmark Gr, 2, 1
  2. Ribes, 2017, Employee turnover prediction and retention policies design: a case study, ArXiv Prepr, ArXiv1707, 01377
  3. Ribeiro, 2016, Model-agnostic interpretability of machine learning, ArXiv Prepr, ArXiv1606, 05386
  4. Ben-Gal, 2003, Context-based statistical process control: a monitoring procedure for state-dependent processes, Technometrics, 45, 293, 10.1198/004017003000000122 · DOI

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Metadata

Authors
Pessach, D., Singer, G., Avrahami, D., Chalutz Ben-Gal, H., Shmueli, E., & Ben-Gal, I.
Affiliations
  • Tel Aviv University (IL)
  • Bar-Ilan University (IL)
Corresponding author
Pessach, Dana
ORCID
Venue
Decision Support Systems
Publisher
Elsevier BV
Published
2020-07
Volume
134
Pages
113290
ISSN
0167-9236 (ISSN-L)
DOI
10.1016/j.dss.2020.113290
PMC
PMC7252110
OpenAlex
W3014109009
Cited by
304
References
61
Citations by year
2026: 46 · 2025: 62 · 2024: 74 · 2023: 57 · 2022: 32 · 2021: 26 · 2020: 7
FWCI
32.96
Type
journal article
Language
en
Open access
Licence
Locations
  • Decision Support Systems (publishedVersion)
  • PubMed Central (publishedVersion)
  • PubMed (publishedVersion)
Funders
Koret Foundation
Topics
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
  • Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
  • Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Keywords
personnel selection, hiring decisions, model interpretability, prescriptive analytics, recruitment optimization, diversity in hiring

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